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Phil. Trans. R. Soc. B (2009) 364, 1593–1605
doi:10.1098/rstb.2008.0334
Evolution in plant populations as a driver of
ecological changes in arthropod communities
Marc T. J. Johnson1,*, Mark Vellend2 and John R. Stinchcombe3
1
Department of Plant Biology, North Carolina State University, Gardner Hall, Raleigh, NC 27695, USA
2
Departments of Botany and Zoology, Biodiversity Research Centre, University of British Columbia,
Vancouver, British Columbia V6T 1Z4, Canada
3
Department of Ecology and Evolution, Centre for the Analysis of Genome Evolution and Function,
University of Toronto, 25 Willcocks Street, Toronto, Ontario M5S 3B2, Canada
Heritable variation in traits can have wide-ranging impacts on species interactions, but the effects
that ongoing evolution has on the temporal ecological dynamics of communities are not well
understood. Here, we identify three conditions that, if experimentally satisfied, support the
hypothesis that evolution by natural selection can drive ecological changes in communities. These
conditions are: (i) a focal population exhibits genetic variation in a trait(s), (ii) there is measurable
directional selection on the trait(s), and (iii) the trait(s) under selection affects variation in a
community variable(s). When these conditions are met, we expect evolution by natural selection to
cause ecological changes in the community. We tested these conditions in a field experiment
examining the interactions between a native plant (Oenothera biennis) and its associated arthropod
community (more than 90 spp.). Oenothera biennis exhibited genetic variation in several plant traits
and there was directional selection on plant biomass, life-history strategy (annual versus biennial
reproduction) and herbivore resistance. Genetically based variation in biomass and life-history
strategy consistently affected the abundance of common arthropod species, total arthropod
abundance and arthropod species richness. Using two modelling approaches, we show that
evolution by natural selection in large O. biennis populations is predicted to cause changes in the
abundance of individual arthropod species, increases in the total abundance of arthropods and a
decline in the number of arthropod species. In small O. biennis populations, genetic drift is
predicted to swamp out the effects of selection, making the evolution of plant populations
unpredictable. In short, evolution by natural selection can play an important role in affecting the
dynamics of communities, but these effects depend on several ecological factors. The framework
presented here is general and can be applied to other systems to examine the community-level
effects of ongoing evolution.
Keywords: coevolution; community evolution; community genetics; extended phenotype;
herbivory; plant–insect
1. INTRODUCTION
It is increasingly recognized that the ecology and
evolution of species interactions within communities
are interdependent (Antonovics 1992; Stinchcombe &
Rausher 2001; Agrawal 2003; Whitham et al. 2006).
On the one hand, species interactions can drive
evolution within populations for traits related to
competitive ability ( Macarthur & Levins 1967;
Grant & Grant 2006), host defence (Ehrlich & Raven
1964; Agrawal 2007), predation (Abrams 2000) and
mutualistic interactions (Bronstein 1994). On the other
hand, evolution within populations is hypothesized to
lead to dynamic ecological changes in the structure and
diversity of communities ( Johnson & Stinchcombe
2007; Urban et al. 2008). Although it is well known
that evolutionary change over macroevolutionary
* Author for correspondence ([email protected]).
Electronic supplementary material is available at http://dx.doi.org/10.
1098/rstb.2008.0334 or via http://rstb.royalsocietypublishing.org.
One contribution of 14 to a Theme Issue ‘Eco-evolutionary dynamics’.
time scales has important consequences for the
ecology of communities ( Webb et al. 2006), it has
only recently been appreciated that evolution might be
an important factor affecting the ecological dynamics of
communities over shorter time scales ( Whitham et al.
2003; Johnson & Stinchcombe 2007), driving ecological changes in communities at a rate comparable
to ecological mechanisms ( Thompson 1998; Hairston
et al. 2005; Ezard et al. 2009).
A combination of recent theory and experiments has
supported the hypothesis that rapid evolution can affect
the ecological dynamics of communities. For example,
the cycles exhibited by predator and prey populations
dramatically change in phase and length when models
allow prey populations to evolve in response to
selection by predators, compared with models that
ignore evolution (Abrams & Matsuda 1997; Jones
et al. 2009). These theoretical predictions have been
corroborated by microcosm experiments that
examined evolution in Escherichia coli attacked by
phage and algae consumed by rotifers (Yoshida et al.
2007). Nevertheless, it is unclear whether the results
1593
This journal is q 2009 The Royal Society
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M. T. J. Johnson et al.
Community responses to plant evolution
from models and laboratory experiments reflect the
dynamics and patterns of natural ecosystems, where
communities are inherently more diverse and are
influenced by many biotic and abiotic factors. Many
recent studies have shown that genetic variation within
a focal population has cascading ecological and
ecosystem-level effects on communities (Shuster et al.
2006; Whitham et al. 2006; Bailey et al. 2009;
Palkovacs et al. 2009), which suggests that evolution
in the focal population has the potential to cause
ecological changes in communities. However, community-level effects of standing genetic variation do not
provide direct evidence that evolution in one population can drive temporal changes in communities
( Johnson & Stinchcombe 2007). The strongest
evidence for supporting the role of evolution by natural
selection in driving community change comes from
experiments that either measure selection on heritable
plant traits shown to influence ecological interactions
among species (present study), or demonstrate an
association between ecotypic differences among focal
populations and corresponding ecological differences
in communities that coexisted with the focal species
during ecotypic differentiation (e.g. Post et al. 2008;
Post & Palkovacs 2009).
Here, we describe and implement an experimental
approach to test the hypothesis that evolution by
natural selection in plant populations can cause
ecological changes in the abundance of particular
arthropod species, as well as the total abundance and
diversity of large arthropod assemblages associated
with plants. This approach involves experimentally
testing a series of necessary conditions of the
hypothesis. Although our data are limited to the study
of plant–arthropod interactions, we believe that this
approach can be applied to any system in which it is
possible to measure phenotypic traits and components
of fitness from a focal population, as well as interactions
between the focal population and other species in
the community.
2. GENERAL APPROACH
For evolution by natural selection in plants to cause
temporal changes in arthropod community variables,
such as the number of species (species richness), their
abundance or species composition, we propose that
three conditions are necessary: (i) a plant population
exhibits genetic variation in a phenotypic trait(s),
(ii) there exists measurable directional selection on
the plant trait(s), and (iii) the traits under selection
cause variation in one or more ecological characteristics
of the arthropod community (e.g. arthropod abundance) associated with the plants. When all three
conditions are satisfied, adaptive evolutionary changes
in plant traits should lead to ecological change(s) in the
arthropod community.
(a) Condition 1: genetic variation in plant traits
Showing that plant populations contain heritable
variation in one or more traits satisfies the first
condition. Plants typically exhibit genetic variation
for multiple traits within and between populations
(Briggs & Walters 1972). Previous research showed
Phil. Trans. R. Soc. B (2009)
that there is genetic variation for morphological,
phenological and putative resistance traits in the
herbaceous plant Oenothera biennis (Onagraceae)
(Johnson & Agrawal 2005; Johnson 2008; Johnson
et al. 2008), which is the focal plant of the experiment
described here. Therefore, in this system and others,
the first necessary condition will usually be satisfied.
(b) Condition 2: directional selection on
plant traits
This condition can be evaluated by measuring the
strength of directional selection acting on plant traits.
These analyses can be performed using conventional
regression techniques that measure the strength of
phenotypic and/or genotypic selection according to the
covariation between relative fitness in a population and
variation in one or more traits (Lande & Arnold 1983;
Rausher 1992). Price (1970) showed that the strength
of selection (S ) acting on a trait is equal to the
covariance (cov(u,z)) between relative fitness (u) and
variation in a trait (z). The response to selection
can then be predicted when S is multiplied by the
heritability of a trait (narrow-sense heritability based on
additive genetic variance [h2 ] for sexual populations
and broad-sense heritability based on additive and nonadditive genetic variances [H 2 ] for inbreeding and
asexual populations) (i.e. RZh2S, the breeder’s
equation). The breeder’s equation can be generalized to
the multivariate case to predict the response to selection
on two or more traits according to the equation:
2
3 2
32 3
G11 G12 . G1j
b1
Dz1
6
7 6
76 7
6 Dz2 7 6 G21 G22 . G2j 76 b2 7
6
7 6
76 7;
ð2:1Þ
6 « 7Z6 «
6 7
«
« 7
4
5 4
54 « 5
Dzi
Gi1
Gi2
.
Gii
bi
where Dzi describes the evolutionary change in the mean
value of trait i across a single generation; the square
matrix G describes the genetic variance (diagonal
elements, Gii); and covariances (off-diagonal elements,
Gij) among traits, and the ‘selection gradient’ bi measures
the strength of selection on trait i (Lande 1979; Lande &
Arnold 1983).
(c) Condition 3: selected traits affect variation
in the arthropod community
To test condition 3, a field experiment is needed to
determine whether genetically variable plant traits
subject to natural selection predict natural variation
in arthropod populations or communities associated
with individual plants. To do this, one can use multiple
regression to determine how community variables are
affected by plant traits; operationally, this can be done
with genotypic means for both the plant traits and the
community variables, to assess whether these relationships have a genetic or environmental basis. A genetic
relationship would indicate an indirect genetic covariance between plant traits and the arthropods found on
those plant genotypes. Analogous indirect genetic
covariances have been studied for social interactions
within populations, which result in indirect genetic
effects that influence the evolution of behavioural traits
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Community responses to plant evolution M. T. J. Johnson et al.
(Moore et al. 1997; Wolf et al. 1997, 1998; Petfield et al.
2005). Models of indirect genetic effects show that the
expression of a phenotype is influenced not only by the
direct genetic and environmental factors experienced
by a focal individual (Moore et al. 1997; Wolf et al.
1998), but also by the indirect effects of the expression
of genes in other individuals in the population.
Inclusion of these indirect genetic effects into quantitative genetics models can alter a population’s
predicted evolutionary response (Moore et al. 1997;
Wolf et al. 1997). This concept was recently extended
to the study of interspecific interactions (Shuster et al.
2006), and our implementation of these ideas here
considers how the selection and genetic variance in one
focal population can influence ecological changes in
other species in a community. Specifically, by calculating the partial regression coefficients (ayi) between
an arthropod community variable y and the selected
plant trait(s) i (hereafter ‘community–trait gradient’;
analogous to j in models of indirect genetic effects, see
Wolf et al. 1998), we can modify the Lande–Arnold
equation to predict how the evolutionary response in
the plant population will lead to an ecological response
in the community. This equation can be written as
2
32 3
G11 G12 . G1j
b1
6
76 7
6 G21 G22 . G2j 76 b2 7
76 7;
½Dc1 Z ½a11 ; a12 ; .; a1i 6
6 «
6 7
«
« 7
4
54 « 5
Gi1
Gi2
.
Gii
bi
ð2:2Þ
where ðDc1 Þ is the change in a single community
variable as a function of the previously described
selection gradients (b vector); the variance–covariance
matrix (G ) of plant traits; and the community–trait
gradients (a vector) describing the relationship
between each plant trait under selection and variation
in the community variable.
(d) Modelling the ecological effects of evolution
on communities
Although conditions 1–3 are necessary for evolution by
natural selection to drive community changes, they are
not sufficient as other evolutionary and ecological
factors (e.g. small population sizes that increase the
strength of genetic drift) may counteract the effects of
selection. Although it would be best to observe directly
ecological changes due to natural selection, such
experiments are exceedingly difficult and we are
unaware of any such study (including our own) done
in the field. Modelling approaches can be useful in such
circumstances and we explore the use of two methods
that use field collected data to predict the effects
of evolution on communities over longer time scales
(see §§3 and 4).
(e) Objectives and assumptions
Our principal objective was to outline and use a
conceptual framework that tests the hypothesis that
evolution by natural selection can drive temporal
changes in the ecological characteristics of communities. To do this, we conducted an experiment that
assesses whether each of the conditions above are
Phil. Trans. R. Soc. B (2009)
1595
satisfied for interactions between the native plant
common evening primrose (O. biennis) and its associated arthropod community. We then use these results
to model the expected evolutionary changes in plant
populations and the predicted ecological effects of this
evolution on: (i) the abundance of common arthropod
species, (ii) the total abundance of all arthropods
on plants, and (iii) the number of arthropod species in
the community.
In many instances, the traits subject to selection
and correlated with variation in the community will
be unknown, making it impossible to test the
conditions outlined above. Using theory from path
analysis (Wright 1934; Kline 2004), we develop a
method of determining when natural selection is acting
on unknown traits in the focal species that cause
variation in community variables. This method uses the
covariance between components of fitness (e.g. survival
and fecundity) in a focal population and variation in an
associated community variable (see §5). Although this
method makes it possible to detect natural selection
that is affecting community variables, it lacks the
mechanistic detail about the particular traits that
are responsible.
As we elaborate below (see §5), the accuracy of our
predictions depends on several factors. Predictions
about changes in the community will be most accurate
when: arthropod populations respond to phenotypic
variation in plant traits consistently over space and
time; the influence of changes in plant phenotype on
the preference and performance of arthropod populations are not outweighed by extrinsically driven
population dynamics; the direct and indirect
interactions among arthropod populations are relatively consistent through time; and rapid coevolution of
arthropod populations does not buffer the communitylevel effects of plant evolution. Because the potential for
these confounds increases through time, our approach
is most valuable at making short-term predictions of
the effects of evolution on community dynamics.
3. MATERIAL AND METHODS
(a) Experimental system
The focal plant species used in our experiment, O. biennis
L. (Onagraceae), is a native herbaceous plant of open habitats
(e.g. fields, roadsides, lakeshores, etc.) in eastern North
America, and it is widely introduced across the world
(Dietrich et al. 1997). Plants typically germinate in spring,
form a basal rosette of leaves and flower at the end of the first
or second year of growth ( Johnson 2007). Because plants are
self-fertilizing and monocarpic (i.e. reproduction is fatal), the
number of fruits represents equal components of total male
and female fitness over a plant’s lifetime. Similar to apomictic
species, O. biennis is functionally asexual as it produces seeds
that are genetically identical to one another and to their
parent plant (Cleland 1972). We used this genetic behaviour
to replicate single genotypes from seed.
Oenothera biennis plays host to over 150 specialist and
generalist herbivorous, omnivorous and predaceous arthropod species ( Johnson & Agrawal 2005, 2007). In the present
experiment, 93 arthropod species naturally colonized
O. biennis over the course of 2 years.
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M. T. J. Johnson et al.
Community responses to plant evolution
(b) Experimental design
This experiment was conducted in 2002 and 2003
at the University of Toronto’s Koffler Scientific Reserve
(www.ksr.utoronto.ca) in South-Central Ontario, Canada.
The design of our experiment is reported elsewhere ( Johnson
& Agrawal 2005, 2007), so we provide only a concise
description here. This paper expands on our previous work
by exploring the untested hypothesis that natural selection on
a plant population can lead to ecological changes in
arthropod communities in the field. Since the main objective
of the present paper was to heuristically outline and
empirically test a conceptual and analytical framework of
the hypothesis, we only use data from one of five habitats
previously studied (i.e. Mesic habitat); results from the other
habitats were similar.
In brief, we germinated seeds from 14 O. biennis genotypes
at the same time and grew plants for five weeks in a
greenhouse at the University of Toronto. We then transplanted plants into an old field leaving the surrounding
vegetation intact. In total, we randomized 184 plants, with
12–15 replicates per genotype, into four contiguous rectangular blocks. We measured six traits from each plant that
characterized the growth rate, resistance to herbivores (early
season damage and leaf toughness), life-history variation
(annual versus biennial reproduction and lifetime biomass)
and fitness (lifetime fruit production) among O. biennis
genotypes. Growth was measured as the maximum rosette
diameter four weeks following germination. Rosette diameter
is an accurate surrogate for early growth rate because all
plants were planted at the same time and O. biennis exhibits
exponential growth during the first five weeks following
germination ( Johnson et al. 2008); rosette diameter is also
highly correlated with total plant biomass (Gross 1981).
Resistance to herbivores was measured as the number of
discrete chewing holes on two rosette leaves made by four
specialist beetles (Tyloderma foveolatum, Tyloderma nigrum,
Graphops pubescens and Altica knabi ) during June of the first
year of growth. The number of holes on two leaves was
correlated with the total number of holes on the entire plant
(rZ0.58, p!0.001, nZ39) and is well correlated with leaf
area consumed (rZ0.95, p!0.001, Johnson & Agrawal
2005). We transformed resistance to ‘relative resistance’ by
taking 1K(no. holes)/(max. no. holes), so that resistance
varied between 0 (low resistance) and 1 (high resistance).
Leaf toughness was measured as the grams of force required
to penetrate a leaf using a force-gauge penetrometer (Type
516; Chatillon, Kew Gardens, NY, USA). Life-history
strategy (annual versus biennial reproduction) was
determined by observing whether plants flowered and died
in 2002 or 2003 as described previously ( Johnson 2007). We
collected the above-ground biomass of plants that flowered in
the autumn of each year, dried the material at 608C for one
week in a forced-air drying oven, and weighed all material to
the nearest 0.1 g. After weighing, we counted the number of
fruits on plants.
We non-destructively surveyed the arthropod assemblage
that naturally colonized each plant. We performed four
surveys each year, every two to three weeks in spring and
summer; surveys were conducted on the same dates in 2002
and 2003, for a total of eight surveys. Arthropods were
visually censused by counting all arthropods on all surfaces of
the plant. Arthropod species were identified by the lead
author and with the assistance of Agriculture Canada’s
National Identification Service (Ottawa, Canada) by taking
specimens from non-experimental plants. We only included
species that were observed to feed directly on O. biennis or to
Phil. Trans. R. Soc. B (2009)
prey on herbivores of O. biennis. For each species, we
determined the maximum abundance across sampling dates
within each year, thus seasonal population dynamics were
decomposed into a single estimate of population size.
Using these data, we extracted several descriptors of
the arthropod community. First, we quantified the five
most common arthropod species (Philaenus spumarius
(Cercopidae and Hemiptera), Cedusa incisa (Cicadellidae
and Hemiptera), Mompha stellella (Momphidae and Lepidoptera), Schinia florida (Noctuidae and Lepidoptera) and
Sparganothis recticulatana (Tortricidae and Lepidoptera)) to
examine whether evolution by natural selection could lead to
changes in the abundance of common arthropod species on
plants. These species comprised approximately 80 per cent of
the arthropod fauna on O. biennis. Second, we calculated the
total abundance of all arthropod species on each individual
plant, by summing the maximum abundances of each species
in each of the 2 years. Because biennials live twice as long as
annuals, we multiplied arthropod abundance by two on all
annual plants to control for differences in plant lifespan.
Finally, we determined the total number of arthropod species
(arthropod species richness) that were identified over the
lifetime of each plant. For species richness, we used only data
from the year in which a plant flowered ( year 1 for annuals,
year 2 for biennials) when calculating community–trait
gradients; therefore, analyses were not biased by differences
in generation time or the number of surveys.
(c) Statistical analyses
(i) Measurement of genetic variance and heritability
We used restricted maximum likelihood (REML) in PROC
MIXED of SAS (SAS Institute, Cary, NC, USA) to estimate
the variance explained by plant genotype for each trait.
The statistical model included plant genotype and spatial
block as random effects, where the significance of genotype
was tested using a log-likelihood ratio test. Because O. biennis
is functionally asexual, a population’s evolutionary response
to selection will depend on the total genetic variance (Vg )
within a population, including both additive and non-additive
components. For this reason, heritability of O. biennis traits
are most accurately estimated as H 2ZVg /VT (Lynch & Walsh
1998), where VT is the total phenotypic variance (genetic
and environmental) in the trait. The coefficient of genetic
variation was calculated as V 0:5
g =mi , where m is the mean for
trait i. The analyses described above were performed on
untransformed data as recommended by Houle (1992).
The variance–covariance trait matrix was calculated using
the genetic variances (Gii ) from REML, and the genetic
covariances (Gij) between traits. Genetic covariances were
calculated according to the equation covgZrg(G11!G22)0.5,
where rg is the Pearson correlation coefficient of the genetic
correlation between the best-linear unbiased predictors
(BLUPs) of the genotypic breeding values (similar to
genotypic means) of two traits. The statistical significance
of genetic covariances was assessed as the p-value for rg
(Lynch & Walsh 1998, p. 641).
(ii) Measurement of natural selection on plant traits
Genotypic multivariate selection analyses were performed
using the Lande–Arnold method (1983) as modified by
Rausher (1992). We first calculated the BLUPs for each plant
trait using the untransformed data. BLUPs were also
calculated for absolute fitness (no. fruits), which was
transformed to relative fitness by dividing the BLUPs for
each genotype by mean fitness among genotypes. We then
regressed relative fitness against the five plant traits and
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Community responses to plant evolution M. T. J. Johnson et al.
selected the best model using forward stepwise regression in
PROC REG of SAS, with an entry/exit p-value of 0.1 and partial
partitioning of the error. Although we explored and present
models with linear (directional selection) and quadratic
(stabilizing and disruptive selection) parameters, we focus
on linear coefficients because directional selection is principally responsible for changes in mean trait values (Lande &
Arnold 1983). Quadratic selection influences the magnitude
of genetic variance within populations (Lande & Arnold
1983; Stinchcombe et al. 2008), and therefore it can affect the
long-term rate at which community variables change in
response to selection on plant traits. Studies that wish to make
long-term predictions about the community-level effects of
natural selection should incorporate the effects of both
directional and nonlinear selection gradients.
Phenotypic selection analyses, where individual plants are
treated as replicates, were also performed and their results are
provided in table 2 in the electronic supplementary material.
Results from genotypic and phenotypic selection analyses
were similar and our focus on the former avoids problems of
confounding effects due to environmentally induced covariance between traits and fitness (Stinchcombe et al. 2002).
(iii) Measurement of community–trait gradients
We calculated the BLUPs for arthropod community variables
associated with each plant genotype and then used multiple
regression to regress individual community variables against
the plant traits subject to natural selection. A significant
partial regression coefficient (a) between a community
variable and a plant trait indicated that the selected plant
trait was associated with or affected the community variable.
We also performed phenotypic analyses as described above;
results are in table 3 in the electronic supplementary material.
(d) Models
We used two complementary modelling approaches (matrix
projections and simulations) to explore the long-term
community-level consequences of evolution. These models
allowed us to examine how community variables are expected
to change in response to selection. The matrix projection
models were most useful in predicting the responses of
community variables to natural selection on plant traits over a
single generation. Our simulation models complemented the
matrix projections as they allowed us to explore the relative
roles of natural selection versus genetic drift in driving
evolutionary and ecological changes under the various
ecological conditions experienced by O. biennis in natural
populations. Specifically, we considered how variation
in population size and population dynamics might influence
O. biennis evolution and changes in the community. The
simulation models also enabled us to explore how nonadditive community variables such as species richness might
change in response to selection on plants; matrix models
cannot accommodate such variables. Assumptions of these
models are discussed below (see §5c).
(i) Matrix projection models
The first approach involved calculating the matrix projections
of equation (2.2) (i.e. DcZaGb) to predict the change in
mean arthropod abundance due to evolution in the O. biennis
population over a single generation. The community–trait
gradients and selection gradients are provided in table 2 and
figure 1, respectively; the G-matrix can be found in table 1 in
the electronic supplementary material. The calculation
of the 95% confidence intervals around Dc is described
in the methods in the electronic supplementary material.
Phil. Trans. R. Soc. B (2009)
1597
Our matrix projection method is most accurate at predicting
changes in absolute and relative abundance over a single
generation because these projections assume constant
(i) genetic variance, (ii) selection and (iii) responses
of community members to plant genetic variation.
This approach cannot be used to model changes in arthropod
species richness because richness is a non-additive composite
measure of species’ presence/absence in a community.
(ii) Simulation models
Our second modelling approach allowed for longer term
projections of changes in arthropod abundance and richness
as a function of evolution in O. biennis. This approach used
our field data on relative genotype fitness, life-history
variation and arthropod assemblages. Using two different
scenarios of O. biennis population dynamics—constant
population size or exponential growth with population
crashes—we simulated evolution by natural selection according to observed relative fitness variation in the field
experiment, and compared the outcomes of these simulations
with simulations in which all plants had equivalent fitness
(i.e. only genetic drift could change genotype frequencies).
A strength of these simulations is that they allow for temporal
changes in the strength of selection and the magnitude of
genetic variation, and they allowed us to model changes in
arthropod abundance and richness across the entire plant
population. Detailed methods of our simulations are available
in methods in the electronic supplementary material.
4. RESULTS
(a) Condition 1: genetic variation in plant traits
We detected significant genetic variation for four of six
plant traits, including lifetime fruit production
(table 1). The broad-sense heritability of traits varied
from 0.001 to 0.44 and the coefficient of genetic
variation ranged between 0.5 and 211 (table 1). This
genetic variation in fitness and other plant traits
indicates that our O. biennis population had the
potential to evolve in response to selection acting on
genetically variable plant traits.
(b) Condition 2: natural selection on plant traits
We detected natural selection on three life-history and
resistance traits (figure 1). There was directional
selection for an increase in lifetime biomass
(figure 1a) and an increase in biennial reproduction
versus annual reproduction (figure 1b). Directional
selection also acted to increase resistance in plants
(figure 1c), even though we did not detect significant
genetic variation in this trait. Stabilizing selection also
acted on biomass (figure 1a). Phenotypic selection
analyses showed significant directional selection for
increased plant biomass and biennial reproduction, but
no selection on resistance (see table 2 in the electronic
supplementary material).
(c) Condition 3: the effect of selected traits on the
arthropod community
Two of the three plant traits with significant genetic
variation and subject to natural selection consistently
predicted variation in community variables (table 2,
figure 2). Genetic variation in plant biomass and/or
flowering strategy significantly predicted variation in
the abundance of four of the five most abundant
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Community responses to plant evolution
Table 1. Genetic variation in six plant traits. (Genetic
variation was measured as genetic variance (Vg ), broadsense heritability ( H 2 ) and the coefficient of genetic variance
(CVg ). Significance tests for the effect of plant genotype were
performed using log-likelihood ratio tests and their p-values
are reported.)
trait
Vg
plant biomass
428.78
leaf toughness
1.40
resistance
!0.01
flowering
2.60
strategy
growth rate
55.50
fruits
3765.60
2
CVg
p-value
0.44
0.002
0.001
0.08
47.24
2.00
0.46
210.88
!0.001
O0.10
O0.10
!0.001
0.36
0.42
17.69
44.41
!0.001
!0.001
H
arthropod species on O. biennis (table 2). For example,
the abundance of P. spumarius increased with greater
plant biomass and a higher frequency of biennial
reproduction (figure 2a,b). There were also positive
effects of increasing bienniality on total arthropod
abundance and arthropod richness (figure 2e,h), as well
as a significant positive relationship between plant
biomass and arthropod richness (figure 2g). We did not
detect any effects of genetic variation in plant resistance
to early season herbivores on arthropod community
variables (figure 2c, f,i ).
(a) 1.8
1.6
relative fitness
M. T. J. Johnson et al.
1.4
1.2
1.0
0.8
0.6
0.4
0.2
20
40
60
biomass ( g)
80
(b) 1.8
1.6
relative fitness
1598
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0
0.2
0.4
0.6
0.8
proportion biennial
1.0
(c) 1.8
(d) Modelling the ecological effects of evolution
on communities
Both the matrix projections and simulation models
indicate that evolution by natural selection can lead to
rapid temporal changes (i.e. within 1–5 years) in
arthropod community variables under some ecological
conditions. Matrix projections of equation (2.2)
predict that the evolutionary response of O. biennis to
selection over a single generation will lead to a
significant increase in the abundance of three arthropod species, as well as the total abundance of
arthropods (table 3). Schinia florida was predicted to
decrease in abundance, but this effect was not
significant. We also used the matrix projections to
predict how the relative abundance of the most
common community members might change as a
function of evolution in O. biennis (see figure 1 in the
electronic supplementary material). These results
suggest that M. stellella will quickly dominate the
community, while the relative abundance of other
species will decrease (e.g. P. spumarius, C. incisa) or
remain unchanged (S. reticulatana).
Our simulations suggest that evolution by natural
selection on O. biennis can lead to rapid temporal
changes in the abundance and species richness of
arthropod communities, but these results depend on
several ecological and evolutionary factors (figure 3). In
large plant populations of constant size, natural
selection on O. biennis leads to a rapid increase in the
total abundance of arthropods, which reaches an
equilibrium after approximately 50 years (figure 3c).
This increase in abundance is significantly greater than
the abundance of arthropods in plant populations
not subject to selection (figure 3c). By contrast,
total arthropod richness significantly decreased in
Phil. Trans. R. Soc. B (2009)
relative fitness
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.8945 0.8950 0.8955 0.8960 0.8965 0.8970
resistance
Figure 1. Directional and stabilizing selection on plant traits.
(a–c) The uncorrected (not partialled) pairwise relationship
between the breeding values of relative fitness and (a) plant
biomass ( bZ0.01G0.005, pZ0.07, rZ0.58; gZK0.001G
0.0003, pZ0.003), (b) flowering strategy (expressed as the
proportion of plants biennial within a genotype; bZ0.91G
0.32, pZ0.02, rZ0.65) and (c) resistance to early season
herbivory. Directional selection gradients (b) were calculated
as partial regression coefficients between relative fitness and
genetic variation in the trait (bZ447.38G166.76, pZ0.02,
rZ0.15); selection gradients were calculated using untransformed data. The selection differential (r) was estimated as
the pairwise correlation between relative fitness and the
normally standardized trait value. The best-fitting quadratic
curve is shown in (a) because directional and stabilizing
selections acted on plant biomass; the strength of stabilizing
selection was estimated as two times the partial regression
coefficient for the quadratic term of biomass.
populations subject to natural selection compared with
populations not experiencing selection (figure 3e).
These dynamics occurred because selection caused
the fixation of the same O. biennis genotype (genotype B)
in every simulation (figure 3a). This selected genotype
was strictly biennial, had high biomass and supported a
large total abundance and richness of arthropods. The
Downloaded from rstb.royalsocietypublishing.org on 4 May 2009
Community responses to plant evolution M. T. J. Johnson et al.
1599
Table 2. Community–trait gradients between community variables and plant traits under selection. (Community–trait gradients
(a) were calculated as the partial regression coefficients between a community variable and genetic variation in plant traits.
Community–trait gradients in bold indicate p%0.05 and italicized values indicate 0.05%p%0.10. The covariance between
relative fitness and normally standardized community variables (covuc) is also shown (significant values in bold). Statistically
significant negative quadratic effects are denoted by † (parameter estimates not shown); we focus on linear effects because they
are primarily responsible for causing changes to the mean of community variables.)
community–trait gradient (a)
community variable
biomass
life-history strategy
resistance
covuc
Philaenus
Cedusa
Mompha
Schinia
Sparganothis
total abundance
total richness
0.09G0.02
0.08G0.01
K0.09G0.26†
0.01G0.02†
0.002G0.01†
0.18G0.29†
0.05G0.02†
4.61G1.09
3.03G0.75
39.65G18.36
K0.88G1.66
1.36G0.58
51.30G21.05
6.64G1.38
877.75G561.11
K202.10G383.29
8930.34G9424.25
K1129.67G852.59
517.23G296.21
9715.06G10804
548.09G710.07
0.75
0.68
0.78
0.23
0.83
0.89
0.84
(b)
(c)
(d)
(e)
(f)
20 (g)
18
16
14
12
10
8
6
(h)
(i)
spittlebug abundance
14 (a)
12
10
8
6
4
2
0
total abundance
80
60
40
total richness
20
20
40
60
biomass (g)
80
0
0.2 0.4 0.6 0.8
proportion biennial
1.0
0.8945 0.8950 0.8955 0.8960 0.8965 0.8970
resistance
Figure 2. Community–trait gradients of the relationship between community variables and plant traits. (a–i ) The relationship
between (a–c) P. spumarius abundance, (d– f ) total arthropod abundance and ( g–i ) arthropod richness, and genetic variation in
plant traits (biomass, proportion biennial and resistance) subject to directional selection. The best-fitting line is shown in figures
where there was a significant linear community–trait gradient; variables exhibiting significant quadratic relationships are
indicated in table 2.
total arthropod richness across the plant population
declined because different plant genotypes supported
unique arthropod assemblages (see Johnson & Agrawal
2007), and natural selection quickly eroded genetic
diversity in the plant population (figure 3a). As a
consequence, although arthropod richness increased on
an individual plant level, it decreased across the entire
plant population (figure 3e).
Phil. Trans. R. Soc. B (2009)
In small populations of a constant size, natural
selection had no discernible effect on the temporal
dynamics of arthropod communities when compared
with non-selected populations (figure 3d,f ). Although
the mean differences in abundance and richness are in
the same direction as in large populations, the 95% CI
values are overlapping (figure 3d, f ). This occurs
because genetic drift swamps out the effects of
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1600
M. T. J. Johnson et al.
Community responses to plant evolution
Table 3. Predicted change in arthropod abundance ðDc1 Þ over
a single generation. (The lower and upper 95% confidence
intervals of Dc1 are also shown. Dc1 was calculated from
equation (2.2) and the derivation of confidence intervals is
described (methods in the electronic supplementary
material). Significant changes at the 0.05 level are shown
in bold.)
community
variable
Dc
Philaenus
14.01
Cedusa
3.66
Mompha
124.16
Schinia
K9.51
Sparganothis
5.85
total abundance 149.39
lower 95%
upper 95%
3.34
K1.78
7.11
K11.39
1.10
13.25
38.52
16.75
402.47
8.05
15.50
469.59
selection, and although populations still evolve to fix a
single genotype, the outcome is more stochastic
(figure 3b). Therefore, evolution due to drift can still
lead to changes in the arthropod community, but these
changes will be unpredictable.
Similar results were found when O. biennis populations were allowed to exponentially increase and
crash, as they often occur in successional and disturbed
landscapes (see figure 2 in the electronic supplementary
material). When populations were large, these
simulations differed in that selection fixed a genotype
(Q ) with greater annual reproduction (figure 2a in
the electronic supplementary material), and although
selection caused arthropod abundance to significantly
increase at first, it eventually declined to the level
observed in populations without selection (see figure 2c
in the electronic supplementary material). As before,
selection in large populations caused a decline in
arthropod richness (see figure 2e in the electronic
supplementary material), and in small populations,
genetic drift caused there to be no significant differences
in abundance or richness between populations with
selection versus without selection (see figure 2d, f in the
electronic supplementary material).
5. DISCUSSION
Using a combination of empirical and modelling
results, we show that evolution in populations of
O. biennis has the potential to drive ecological changes
in the arthropod community associated with this
plant. This conclusion follows from several results:
(i) O. biennis exhibits significant genetic variation for
multiple plant traits (condition 1), (ii) directional
selection acts on life-history and resistance traits of
O. biennis (condition 2), and (iii) two of the selected
plant traits predict variation in the abundance of
individual arthropod species, total arthropod abundance and the number of arthropod species on plants
(condition 3). Unfortunately, few field studies have
attempted to understand whether evolutionary change
in natural populations causes ecological changes to
communities over time ( Fenner & Ross 1994;
Fussmann et al. 2007). Our simulations predict that
evolution due to natural selection on O. biennis
populations will lead to rapid increases in the abundance
Phil. Trans. R. Soc. B (2009)
of common arthropod species, and a decrease in the
total number of arthropod species in plant populations.
These results will probably only hold in large O. biennis
populations where selection is most efficient.
(a) Evolutionary mechanisms
We have focused on natural selection as a driver of
ecological change, but any of the main mechanisms of
evolution (i.e. natural selection, genetic drift, dispersal,
mutation and assortative mating) can potentially cause
ecological changes in communities. Our simulations
showed that the effects of selection lead to predictable
community changes in large plant populations
(figure 3c,e), while in small populations genetic drift
caused stochastic community dynamics (figure 3d, f ).
Although we did not manipulate dispersal or mutation,
they can have important effects on the evolution of
populations (Moore & Hendry 2009) and potentially
even the ecological changes in communities (Garant
et al. 2007). When an individual disperses or a new
mutant arises in a population, the effect of this variant on
the rate and direction of evolution (and the subsequent
effect on the community) will depend on the variant’s
phenotype. If the phenotype of a new mutant or
dispersed individual represents a random representation of existing phenotypes in the population, then
increased mutation or dispersal rates will attenuate the
difference between selected and non-selected populations and decrease the community-level effects of
adaptive evolution. If new variants are not random and
bring a population closer to its optimum, then
increasing dispersal and mutation rates will increase
the rate of evolution and accentuate the effects of
selection on community dynamics. Finally, if selection
or drift depletes genetic diversity within populations,
dispersal and mutation will increase genetic variation
and potentially fuel ongoing adaptive evolution and any
effects this evolution has on the community.
(b) Selection and genetic diversity
Our simulations suggest that selection on O. biennis
populations will erode genetic diversity and cause a
decrease in the richness of arthropod species in plant
populations. This result provides support for the
prediction that evolution can be an important
mechanism driving community-level effects of genetic
diversity ( Hughes et al. 2008). Although strong
selection is predicted to erode genetic variance in any
closed population (Barton & Turelli 1989), our results
are likely to be most accurate for highly selfing and
asexual populations. By contrast, sexual populations
are likely to maintain greater genetic diversity within
populations, which will allow ongoing evolution that
can influence community processes. Also, balancing
selection that maintains genetic diversity in plant traits
has the potential to positively influence community
diversity through time (Lankau & Strauss 2007).
(c) Assumptions and caveats to studying the
effects of evolution on communities
Several caveats arise when studying the communitylevel effects of evolution. For instance, although we
satisfied the necessary conditions of our hypothesis,
and our models suggest that ecological effects
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Community responses to plant evolution M. T. J. Johnson et al.
genotype frequency
1.0 (a)
1601
(b)
0.8
B
0.6
0.4
Q
other
F
0.2
drift
0
1.3 ×105 (c)
2500 (d )
total abundance
total abundance
1.2 ×105
1.1 ×105
1.0 ×105
9.0 ×104
8.0 ×104
6.0 ×104
1000
0
60 ( f )
100 (e)
arthropod richness
90
arthropod richness
1500
500
7.0 ×104
80
70
60
50
40
30
2000
0
50
100
year
150
200
50
40
30
20
10
0
50
100
year
150
200
Figure 3. Simulated evolution and community change in large (a,c,e; nZ2800) and small (b, d, f ; nZ28) O. biennis populations.
(a,b) We contrasted temporal changes in genotype frequencies in populations subject to selection (i.e. relative fitness of
genotypes determined according to our experimental results; see methods in the electronic supplementary material) to
populations where all genotypes had equal relative fitness and were therefore only influenced by genetic drift. We show the mean
genotype frequencies (across 1000 simulations) of the three most fit genotypes in selected populations (B, F and Q ), the mean
frequency of all other genotypes in selected populations (‘other’ line) and the mean frequency of genotypes (‘drift’ line) in drift
populations. (c,d ) Mean total arthropod abundance and (e, f ) mean arthropod richness summed across all plants in the
population are shown from selected (bold solid curve) and drift (bold dotted curve) populations; thin solid and dotted curves
show the upper and lower 95% CI around the mean selected and drift populations, respectively.
of evolution on the community are likely to occur in
large O. biennis populations, several factors may still
prevent ecological changes in arthropod communities
in response to plant evolution. First, many factors other
than plant genotype influence arthropod communities
(Strong et al. 1984), which may render the influence of
changes in genotypic composition unimportant unless
arthropods respond to the phenotypic distribution of
plant traits consistently over space and time. If
arthropod populations change their preferences for
different phenotypes depending on the environment,
plant evolution will have little effect on their ecology.
Even when arthropods do respond to plant phenotypic
variation in a consistent way, the response of arthropod
populations to other ecological factors (e.g. climatedriven population dynamics) could obscure the effects
of evolution. Second, our approach does not explicitly
incorporate the interactions between arthropod species
and how they might change, but instead assumes that
Phil. Trans. R. Soc. B (2009)
any interactions between species that affected the
observed abundances will remain the same over time,
regardless of changes in genotype frequencies. Arthropod populations can directly and indirectly influence
the preference and performance of other arthropods on
plants (Kaplan & Denno 2007), and changes in the
relative abundance of one population may have
cascading effects throughout the community, dampening or amplifying the community-level effects
of evolution ( Whitham et al. 2006; Bailey et al.
2009). Finally, arthropod populations can rapidly
adapt to plant phenotypes ( Van Zandt & Mopper
1998), or exhibit plastic behavioural and physiological
responses that could buffer ecological changes
(Karban & Agrawal 2002).
These caveats arise because unlike a plant’s phenotype, which is influenced directly by a plant’s
genotype and changes in gene expression due to the
environment, communities are composed of multiple
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M. T. J. Johnson et al.
Community responses to plant evolution
individual populations, each with their own capacity
to ecologically and evolutionarily respond to their
environment. As such, understanding the biology
underlying these caveats will be necessary to decipher
when and where the effects of evolution on communities are likely. We believe that our predictions will be
most accurate at making short-term predictions about
the effects of evolution on communities.
It is possible to make predictions about the response
of multiple arthropod populations while explicitly
accounting for the effects of interactions among
species. This requires estimating the genetically
controlled covariance in abundance between each of y
arthropod species and adding the resulting y!y matrix
as the first element of equation (2.2). The left-hand
side of this revised equation would be a response vector
with elements describing the simultaneous changes in
the abundance of y arthropod species; the community–
trait gradient matrix would similarly have y rows.
(d) What to do when the traits causing variation
in the community are unknown
In many instances, the selected traits associated with
variation in the community will be unknown, making it
difficult to test the conditions outlined above. It would
still be useful, however, to have a method that
determines whether community-level consequences of
evolution are possible or likely in a given system. We
have used theory relating to path analysis to derive such
a method.
Consider the path diagram of interactions in
figure 4a, which parallel the interactions observed in
our study. In this example, only two plant traits A and B
are subject to natural selection with path coefficients
(selection gradients) bA and bB, respectively. These
same two traits causally affect a community variable c
with community–trait gradients aA and aB. In this
example, when all of the variations in u and c are
explained by A and B, we can use path analysis to
derive an empirical relationship between these variables. In particular, Wright (1934) showed that the
correlation between any two variables that are not
causally related, such as u and c, can be estimated
according to:
ruc Z Sbiu ric ;
ð5:1Þ
where b iu represents the normally standardized
regression/path coefficient between variable i and u;
and ric is the correlation coefficient between trait i and
the community variable c. When all trait and community variables are transformed to normal variates, with
mean 0 and standard deviation 1, the correlation
between any two variables i and j has the property that
r ijZbijZcovij. It then follows that the covariance
between relative fitness in one population and variation
in a community variable (covuc) can be expressed as
covuc Z covAu covAc C covBu covBc :
ð5:2Þ
Note that the right-hand side of the equation
is equivalent to the multiplication of ab from equation
(2.2) in the case of normally standardized coefficients.
Therefore, anytime relative fitness in one population
covaries with a community variable (figure 4b), covuc
Phil. Trans. R. Soc. B (2009)
(a)
bA
A
bB
w
covwc
aA
B
aB
c
(b)
relative fitness (w)
1602
community variable (c)
Figure 4. (a,b) Path diagram illustrating the relationship
between plant traits, relative fitness and community variation.
Two plant traits A and B causally influence variation in
relative fitness (u) according to path coefficients bA and bB.
These same traits influence variation in a community variable
(c) according to coefficients aA and aB. The resulting
covariance between u and c is denoted by covuc.
provides evidence that natural selection is acting on
traits that affect or are associated with community
variables. It is important to note that covuc provides an
estimate of total selection on all unmeasured traits that
affect the community variable, as equation (5.2) is a
specific case of equation (5.1) and generalizes to any
number of traits. Thus, estimating the genetic covariance between relative fitness and community variables
provides useful information irrespective of the presence
or absence of the community–trait gradients. However,
the accuracy of inferences based on this covariance (or
point estimates of the covariance term itself, if only
phenotypic data are available) will be related to how
much variation in u and c is explained by other
ecological variables as well (e.g. seasonal weather
change), and a complete mechanistic understanding
of how and why this process is occurring can only be
gained through the trait-based approach described in
equation (2.2).
(e) Practical considerations in studying the effects
of evolution on communities
The most convincing evidence in support of our
hypothesis will come when there is direct experimental
evidence that evolution by natural selection in one
population has caused temporal changes in the ecology
of one or more populations of other species in a natural
community. Testing this hypothesis in the field presents
researchers with practical challenges, and we outline
two potential experimental protocols.
One experiment involves manipulating selection on
a focal species in the field and observing its evolution
and the ecological effects of evolution on the community. The optimal design is one where there are
replicated experimental populations that are of identical size and genotypic composition at the beginning of
Downloaded from rstb.royalsocietypublishing.org on 4 May 2009
Community responses to plant evolution M. T. J. Johnson et al.
the experiment. Half of the populations are then
randomly assigned to be selection lines (e.g. removal
of herbivores), while the other half are controls
(e.g. herbivores present). It is then straightforward to
compare phenotypic traits and community variables
between selected and control lines to examine divergence. This method will work best when selection and
control lines are later assessed in a common environment, allowing plastic and evolutionary responses to be
distinguished. Another alternative would be to follow
changes in genotype frequencies using molecular
markers (in the case of asexual/selfing species) or
changes in allele frequencies for ecologically relevant
Mendelian traits (Subramaniam & Rausher 2000;
Barrett et al. 2008). If there are concurrent changes
in the size of the focal populations, then it will be
further necessary to parse out ecological and evolutionary effects on communities (Hairston et al. 2005; Ezard
et al. 2009).
An alternative approach is to experimentally
simulate evolution by manipulating changes in the
phenotypic distribution of traits within a population
over time. Using this method, the phenotypic distribution of ecologically important traits could be
altered every generation to simulate directional,
disruptive or stabilizing selection on a population.
Community dynamics in ‘evolved’ and control
(static) plant populations could then be contrasted
to determine the community consequences of the
simulated phenotypic evolution. This approach mitigates the practical challenges of following genotype/
allele frequencies and concurrent ecological changes
within the selected populations.
6. CONCLUSION
Understanding the importance of evolution for the
ecological dynamics of communities is an important,
yet unresolved problem in evolutionary ecology.
Circumstantial evidence is mounting that evolution in
one population can cause ecological changes to natural
communities (Agrawal 2005; Lankau & Strauss 2007;
Post et al. 2008; Post & Palkovacs 2009). However, no
study has convincingly demonstrated that evolution
drives rapid ecological changes in natural communities
over time. Our own data and simulations suggest that
natural selection on genetic variation in life-history
traits of a native plant can drive rapid ecological
changes in the abundance and species richness of
arthropod communities. The experimental and analytical framework proposed here could lead to a better
understanding of the community-level effects of
evolution by natural selection in a wide diversity of
experimentally tractable systems.
We thank A. A. Agrawal, J. Bailey, M. W. Blows, I. Hanskii,
E. Hine, A. Hendry, R. Lande, A. MacDonald, J. Schweitzer
and N. Turley for constructive criticisms on the paper, and to
D. Garant, A. Hendry and F. Pelletier for organizing the
special issue. M.T.J.J., M.V. and J.R.S. were funded by
the Natural Sciences and Engineering Research Council of
Canada. J.R.S. also acknowledges funding from the Canadian
Foundation for Innovation and University of Toronto.
Phil. Trans. R. Soc. B (2009)
1603
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